import tensorflow as tf
from tensorflow import keras
import numpy as np

imdb = keras.datasets.imdb
word_index = imdb.get_word_index()
(train_data, train_labels), (test_data, test_labels) = imdb.load_data(num_words=10000)

word_index = {k:(v+3) for k,v in word_index.items()}
word_index["<PAD>"] = 0
word_index["<START>"] = 1
word_index["<UNK>"] = 2  # unknown
word_index["<UNUSED>"] = 3

reverse_word_index = dict([(value, key) for (key, value) in word_index.items()])
def decode_review(text):
    return ' '.join([reverse_word_index.get(i, '?') for i in text])

train_data = keras.preprocessing.sequence.pad_sequences(train_data, value=word_index["<PAD>"], padding='post', maxlen=256)
test_data = keras.preprocessing.sequence.pad_sequences(test_data, value=word_index['<PAD>'], padding='post', maxlen=256)

vocab_size = 10000
model = keras.Sequential()
model.add(keras.layers.Embedding(vocab_size, 16))
model.add(keras.layers.GlobalAveragePooling1D())
model.add(keras.layers.Dense(16, activation=tf.nn.relu))
model.add(keras.layers.Dense(1, activation=tf.nn.sigmoid))
model.summary()

model.compile(optimizer=tf.train.AdamOptimizer(), loss='binary_crossentropy', metrics=['accuracy'])
x_val = train_data[:10000]
partial_x_train = train_data[10000:]

y_val = train_labels[:10000]
partial_y_train = train_labels[10000:]
history = model.fit(partial_x_train, partial_y_train, epochs=40, batch_size=512, validation_data=(x_val, y_val), verbose=1)
result = model.evaluate(test_data, test_labels)

# 推测影评结果
predictions = model.predict(test_data)

label = ['负面评价', '正面评价']
# i = 2
# for i in range(0, len(test_data) - 1):
#     print("第%d条数据"%(i))
#     print(decode_review(test_data[i]))
#     print('真实测试数据影评是： ', label[test_labels[i]])
#     print('评测结果： ', predictions[i])
#     print('测试影评是： ', label[int(round(predictions[i][0]))])

history_dict = history.history
print(history_dict.keys())